NDLI: Prediction stability in a data-based, mechanistic model of σF regulation during sporulation in Bacillus subtilis

Content Provider	PubMed Central
Author	Georgios, Fengos Dagmar, Iber
Copyright Year	2013
Abstract	Mathematical modeling of biological networks can help to integrate a large body of information into a consistent framework, which can then be used to arrive at novel mechanistic insight and predictions. We have previously developed a detailed, mechanistic model for the regulation of σ F during sporulation in Bacillus subtilis. The model was based on a wide range of quantitative data, and once fitted to the data, the model made predictions that could be confirmed in experiments. However, the analysis was based on a single optimal parameter set. We wondered whether the predictions of the model would be stable for all optimal parameter sets. To that end we conducted a global parameter screen within the physiological parameter ranges. The screening approach allowed us to identify sensitive and sloppy parameters, and highlighted further required datasets during the optimization. Eventually, all parameter sets that reproduced all available data predicted the physiological situation correctly.
Related Links	http://dx.doi.org/10.1038/srep02755
Starting Page	2755
File Format	PDF
ISSN	20452322
e-ISSN	20452322
Journal	Scientific Reports
Volume Number	3
Language	English
Publisher	Nature Publishing Group
Publisher Date	2013-09-01
Access Restriction	Open
Rights Holder	Nature Publishing Group
Subject Keyword	Science and technology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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Prediction stability in a data-based, mechanistic model of σF regulation during sporulation in Bacillus subtilis